5 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
SMART: A Spectral Transfer Approach to Multi-Task Learning
Boxin Zhao, Mladen Kolar, Jinchi Lv
Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from rela…
Personalized Binomial DAGs Learning with Network Structured Covariates
Boxin Zhao, Weishi Wang, Dingyuan Zhu +5
The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez +1
High-quality machine learning models are dependent on access to high-quality training data. When the data are not already available, it is tedious and costly to obtain them. Data m…
L-SVRG and L-Katyusha with Adaptive Sampling
Boxin Zhao, Boxiang Lyu, Mladen Kolar
Stochastic gradient-based optimization methods, such as L-SVRG and its accelerated variant L-Katyusha (Kovalev et al., 2020), are widely used to train machine learning models.The t…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Ziqi Liu +4
Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling…
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Filip Hanzely, Boxin Zhao, Mladen Kolar
We investigate the optimization aspects of personalized Federated Learning (FL). We propose general optimizers that can be applied to numerous existing personalized FL objectives,…